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Bill & Melinda Gates Foundation

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Grand Challenges is a family of initiatives fostering innovation to solve key global health and development problems. Each initiative is an experiment in the use of challenges to focus innovation on making an impact. Individual challenges address some of the same problems, but from differing perspectives.

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Toward a Permanent Influenza Vaccine: Design of Hemagglutinin Antigen Analogs and Vaccination Protocols by Structural Modeling, Atom-Based Simulations, Machine Learning, and Experiments

Martin KarplusHarvard UniversityCambridge, Massachusetts, United States
Grand Challenges
Influenza Vaccine
29 Aug 2019

Combining Epitope-Based Vaccine Design with Informatics-Based Evaluation to Obtain a Universal Influenza Vaccine

Rebeca SalmeronFoundation for the National Institutes of Health IncNorth Bethesda, Maryland, United States
Grand Challenges
Influenza Vaccine
28 Aug 2019

Turning Influenza into Measles via Mosaic Natural Selective Targeting of Immune Responses (MONSTIR)

Patrick WilsonUniversity of ChicagoChicago, Illinois, United States
Grand Challenges
Influenza Vaccine
28 Aug 2019

An Unconventionally MHC-Restricted T Cell Vaccine for Influenza

Jonah SachaOregon Health and Science UniversityPortland, Oregon, United States
Grand Challenges
Influenza Vaccine
20 Aug 2019

Digital Immune Optimized and Selected Universal Influenza Vaccine Antigens (DIOS-UIVA)

Jonathan HeeneyUniversity of CambridgeCambridge, United Kingdom
Grand Challenges
Influenza Vaccine
13 Aug 2019

Rational Design of a Universal Flu Vaccine Using Recombinant Neuraminidase

Alice McHardyThe Helmholtz Centre for Infection ResearchBraunschweig, Germany
Grand Challenges
Influenza Vaccine
23 Jul 2019

Real-Time Genomic Epidemiology and Improved Data Sharing to Control Middle East Respiratory Syndrome (MERS-CoV)

David AanensenUniversity of OxfordOxford, United Kingdom
Grand Challenges
Annual Meeting Call-to-Action
23 May 2019

David Aanensen from the University of Oxford and the Wellcome Sanger Institute in the United Kingdom and Maria van Kerkhove of the World Health Organization in Switzerland will combine next generation DNA sequencing technology with a simple, web-based data collection, processing, and distribution platform to better track the global spread of deadly infectious diseases including Middle East Respiratory Syndrome (MERS-CoV). MERS - also known as camel flu - is a viral disease that causes fever, cough, diarrhea, and shortness of breath, and is transmitted from camels to humans. One third of people diagnosed with the disease die. Next generation sequencing (NGS) technology allows rapid, inexpensive detection of pathogens as they spread. However, laboratories in different member states use different formats for sequencing data, and there is no mechanism for sharing it in real time. This limits the value of the technology for stopping outbreaks. To address this, they will establish routine sequencing protocols for both human and camel samples, and develop an interactive web platform on which the sequencing and epidemiological data can be shared. This will help develop more effective, real-time medical and non-medical interventions at local, national, and international levels. Once established, the protocols developed here may be applied to outbreaks of other diseases.

A Systems Level Approach to Crop Health

David HughesPennsylvania State UniversityUniversity Park, Pennsylvania, United States
Grand Challenges
Annual Meeting Call-to-Action
23 May 2019

David Hughes of Pennsylvania State University, John Corbett of aWhere, and Rhiannan Price of DigitalGlobe, in the U.S. will develop a software platform comprising prediction algorithms that leverage artificial intelligence to predict where and when plant diseases and pests will occur from weather and satellite data to alert farmers to check their crops. Pests and diseases are moving targets, however most current surveillance methods monitor only their presence or absence. Predicting when and where they are likely to occur would be more valuable for preventing them. This has recently been made possible by studies on how environmental factors influence the emergence and behaviour of crop pests and diseases. They will use a systems approach that incorporates these new predictors along with historical data and couples them with an artificial intelligence component that learns from ground observations recorded using smartphones to improve accuracy. They will combine their existing agricultural intelligence platform and smartphone application with their prototype predictive model and test their approach with maize and cassava crops on farms across seven different counties in Kenya. The platform will produce location-specific forecasts that can be acted upon immediately by farmers.

Deep Learning (AI) for Histology of Onchocerciasis

Achim HoeraufUniversity Hospital BonnBonn, Germany
Grand Challenges
Annual Meeting Call-to-Action
3 May 2019

Achim Hoerauf of IMMP in Germany will apply artificial intelligence (AI) to speed the development of treatments for onchocerciasis, which is an infectious disease commonly known as River Blindness caused by a parasitic worm. The parasites are spread by affected blackflies, and the worm larvae accumulate in the skin and eyes, causing irritation and sometimes blindness. Nearly 21 million cases occur each year, and 99% of affected people live in Africa. The drug currently used for treatment kills only worm larvae, and studies are ongoing to identify more effective drugs that target adult worms. However, evaluating these drug candidates requires manual analysis using microscopy of samples of irritated skin from patients after treatment. This process is time consuming and slows drug development. To address this, they will use samples that have already been manually annotated to train an AI system to automatically analyze future samples to recognize worm body parts, gender, vitality, and stage of development. Once established, the AI system will be tested with samples from a new clinical trial - tissues from patients treated with the new drug will be analyzed in parallel by human and computer. Once optimized, the AI system will take over the analysis, and the much slower human analysis will only be needed as a quality control system.

PET/CT Signatures to Optimize Tuberculosis Host-Directed Therapy (HDT) Development

Yingda XieRutgers, The State University of NJNewark, New Jersey, United States
Grand Challenges
Annual Meeting Call-to-Action
3 May 2019

Yingda Xie of Rutgers, The State University of NJ and JoAnne Flynn of the University of Pittsburgh, both in the U.S., will develop a non-invasive approach for testing candidate anti-tuberculosis compounds in animal models and patients using positron emission tomography-x-ray computed tomography (PET/CT). Tuberculosis (TB) is a leading cause of death in developing countries, and rates are sustained by the causative bacterium, Mycobacterium tuberculosis, developing resistance to current drugs. To circumvent this, new drugs are being designed to target human cells and proteins rather than those of the bacteria. To test these drugs, new tools are also needed to monitor TB in patients. 18 Fluorodeoxyglucose (FDG)-PET/CT is a non-invasive imaging tool that uses radioactively-labelled glucose to light up areas of metabolic activity in the body such as the lesions formed by M. tuberculosis and immune cells that play a critical role in infection. They have histopathological sections and cell and chemical data of TB lesions from non-human primate models and will use them to quantify the different lesions. Then, by using the available PET-CT scans of the lesions, they will search for quantitative signatures that can predict a specific type of lesion. The accuracy of these PET/CT signatures will be tested in a separate group of animals. Their study will reveal details of the TB immune response across different lesions, which could help design new treatments, and the signatures can be used to test the activity of new drug candidates in animal models and humans.

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